This assignment is for ETC5521 Assignment 1 by Team Hakea comprising of Dang Thanh Nguyen, Rui Min Lin, (Siddhant V Tirodkar), and (Varsha Ujjinni Vijay Kumar).

1 Introduction and motivation

Coffee is the most popular beverage around the world, and as a dominant beverage, people are attracted not only by its taste, but also its continuous refreshing effect that helps people stay focused. Let’s see, you’re reading this with a cup of coffee, aren’t you?

The coffee culture is gradually developed when it is first introduced in 15th century, and facilitates the practice of coffee tasting. Coffee tasters measures coffee from various aspects and rated the coffee on a scale of 0-100 ultimately. We are curious about why these coffee considered the best, which, the different factors that elucidate the quality of the coffee cultivated and the country with the best graded coffee beans.

The data is originally observed from Coffee Quality Institute website and was scraped by James DeLoux, the data was then re-posted on Kaggle. Furthermore, it is then cleaned by Thomas Mock.

The cleaned data set consists of 1339 observations and 43 variables. And some potential limitations are: * As the number of graded coffee beans differ largely from country to country, some of the analysis will be biased. * For US, there are 3 areas that produce coffee beans: Mainland, Puerto Rico and Hawaii. In this research, the researcher merge all this areas together to better represent the country.

2 Data description

The original data is a data frame scraped by James LeDoux in January 2018 from the Coffee Quality Institute website which has a few missing values columns within it, so the author has cleaned the data set by removing the variables: “view_certificate_1”, “view_certificate_2”,etc.

Data wrangling and cleaning is crucial to produce an exploratory data analysis fluently. Originally there are two separate data set raw_robusta and raw_arabica. Thomas Mock first cleaned the variable names in both data set with function janitor::clean_names, and inappropriate data class is corrected using col_double, col_character etc. Variables like salt_acid, bitter_sweet, fragrance_aroma, mouthfeel, and uniform_cup is renamed to acidity, sweetness, aroma, body and uniformity respectively, to allow a better understanding for readers.

The data sets were then joined by implementing the function bind_rows to produce the merged data set, which is exported to a single csv file “coffee_ratings.csv” with 1339 observations and 43 variables.

2.1 Structure of data

After this knowing what each of those variables define with respect to our topic is important so here are a few variables we should know the general meaning to in accordance with coffee:

Variable Class Description
total_cup_points double Total rating/points (0 - 100 scale)
species character Species of coffee bean (arabica or robusta)
owner character Owner of the farm
country_of_origin character Where the bean came from
farm_name character Name of the farm
lot_number character Lot number of the beans tested
mill character Mill where the beans were processed
ico_number character International Coffee Organization number
company character Company name
altitude character Altitude - this is a messy column - I’ve left it for some cleaning
region character Region where bean came from
producer character Producer of the roasted bean
number_of_bags double Number of bags tested
bag_weight character Bag weight tested
in_country_partner character Partner for the country
harvest_year character When the beans were harvested (year)
grading_date character When the beans were graded
owner_1 character Who owns the beans
variety character Variety of the beans
processing_method character Method for processing
aroma double Has both fragrance (ground beans) and aroma (hot water with coffee powder)
flavor double Flavor grade
aftertaste double Length of positive flavor remaining after the coffee is swallowed
acidity double The score depends on the origin characteristics and other factors(degree of roast)
body double Body grade
balance double Balance grade
uniformity double Refers to the consistency of flavor . 2 points are awarded for each cup displaying this attribute, with a maximum of 10 points if all 5 cups are the same.
clean_cup double Refers to a lack of interfering negative impressions from first ingestion to final aftertaste
sweetness double Sweetness grade
cupper_points double The cupper marks the intensity of the Aroma on a scale
moisture double Moisture Grade
category_one_defects double Full black or sour bean, pod/cherry, and large or medium sticks or stones(count)
quakers double Unripened beans that are hard to identify during hand sorting and green bean inspection
color character Color of bean
category_two_defects double Parchment, hull/husk, broken/chipped, insect damage, partial black or sour, shell, small sticks or stones, water damage(count)
expiration character Expiration date of the beans
certification_body character Who certified it
certification_address character Certification body address
certification_contact character Certification contact
unit_of_measurement character Unit of measurement
altitude_low_meters double Altitude low meters
altitude_high_meters double Altitude high meters
altitude_mean_meters double Altitude mean meters

  • Aroma(aroma grade) : Has both fragrance (ground beans) and aroma (hot water with coffee powder)

  • Aftertaste : length of positive flavor remaining after the coffee is swallowed.

  • Acidity : the score depends on the origin characteristics and other factors(degree of roast)

  • Uniformity : refers to the consistency of flavor . 2 points are awarded for each cup displaying this attribute, with a maximum of 10 points if all 5 cups are the same.

  • Clean_cup : refers to a lack of interfering negative impressions from first ingestion to final aftertaste, a “transparency” of cup. 2 points are awarded for each cup displaying the attribute.

  • Cupper-points : The cupper marks the intensity of the Aroma on a scale. A final score for Fragrance and Aroma is now given on the basis of a combined evaluation of Fragrance and Aroma.

  • Category 1 defect: Full black or sour bean, pod/cherry, and large or medium sticks or stones.

  • Category 2 defect: Parchment, hull/husk, broken/chipped, insect damage, partial black or sour, shell, small sticks or stones, water damage.

  • Quakers: Quakers are unripened beans that are hard to identify during hand sorting and green bean inspection.

2.2 Questions of interest

The aim of this report is to discover characteristics within best-graded coffee bean countries, and will examine from different aspects of coffee beans to explore the likely factors that influence its quality and taste.

Secondary question:

  1. Which Country produces the best quality coffee beans?

  2. Does Altitude really affect the quality of the beans produced?

  3. Which countries perform best on individual grading criteria such as aroma, acidity, sweetness etc?

  4. Which regions/companies perform better than others in the quality of the coffee beans produced, intra-country?

  5. Compare the characteristics of Arabica and Robusta, what insights can be discovered?

  6. Are there any similarities in the processing method of coffee beans amongst the best-graded coffee beans countries?

3 Explatory Data Analysis

Coffee beans are harvested, produced and exported throughout almost every country in the world. This dataset contains the data of Ethiopia, Guatemala, Brazil, Peru, United States, United States (Hawaii), Indonesia, China, Costa Rica, Mexico, Uganda, Honduras, Taiwan, Nicaragua, Tanzania, United Republic Of, Kenya, Thailand, Colombia, Panama, Papua New Guinea, El Salvador, Japan, Ecuador, United States (Puerto Rico), Haiti, Burundi, Vietnam, Philippines, Rwanda, Malawi, Laos, Zambia, Myanmar, Mauritius, Cote d?Ivoire, NA, India. We will be focusing on the manufacturing and the quality aspect of the beans produced in this report. The two main variants of a coffee bean are Arabica and Robusta. Approximately 60% of coffee produced in the world is Arabica and approximately 40% is Robusta. Arabica beans consists about 0.8%-1.4% caffeine and Robusta beans consists of 1.7%-4% caffeine. Coffee is one of the most important cash crop in the world. Wikipedia

3.1 Best Quality Beans

The Coffee Quality Institute is a non-profit organization that grades coffee samples from around the world in a consistent and professional manner.

The coffee beans are graded by the Coffee Quality Institute’s trained reviewers. The total rating of a coffee bean is a cumulative sum of 10 individual quality measures: aroma, flavour, aftertaste, acidity, body, balance, uniformity, clean cup, sweetness and cupper points. Each grade is on a 0–10 scale resulting to a total cupping score between zero and one hundred. Figure 3.1 aims to address the primary question Which country produces best quality coffee beans?. X axis shows the country while Y axis denotes the overall rating achieved by the coffee bean. It is clear that Ethiopia produced the highest quality of coffee beans. However, it is interesting to note that there is not much variation between countries as most of them have median score of around 80-85 points. Thus, We can conclude that based on the dataset, there is not much difference in coffee quality between countries, with Ethipoia produces the highest-quality beans.

Figure 3.1: Boxplot for total ratings of coffee beans by country

3.2 Altitude v/s Quality

In short, There is a slight, very very veryyyy small relation - Thomas

Regression model for Altitude

Figure 3.2: Regression model for Altitude

3.3 Processing Method v/s Quality

To check if the processing method affects the quality of coffee beans produced, we have taken help of the ANOVA test as the processing method is a categorical variable. The ANOVA test returns p-values very far away from the confidence interval of 5% which can be observed when we plot the residuals against the fitted values in Figure 2 . Hence it is established that the processing methods used in producing the coffee beans does not influence the quality of beans produced.

##                     Df Sum Sq Mean Sq F value Pr(>F)  
## processing_method    4     58  14.468   1.978 0.0956 .
## Residuals         1164   8514   7.314                 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 170 observations deleted due to missingness
ANOVA test for processing methods vs Quality

Figure 3.3: ANOVA test for processing methods vs Quality

3.4 Defects v/s Quality

After the linear model for altitude turned out to be insignificant, we figured there are several other variables in the dataset that we could try fitting a model. The dataset contains category one and category two defects which are also known as primary and secondary defects and we fitted a muti-variate model using the same. The model after considering both the variables return a p-value very close to 0 and hence this model is considered a good one and as can be seen in Figure 3 which suggest that almost all the residuals reside very close to the 0 line with a very few outliers. Thus it can be understood that defects influence the quality of coffee beans produced.

Table 3.1: Defects linear regression model
term estimate std.error statistic p.value
(Intercept) 82.5885115 0.1122747 735.593003 0.00000
category_one_defects -0.1106967 0.0378995 -2.920796 0.00355
category_two_defects -0.1252918 0.0181894 -6.888192 0.00000

3.5 Individual Criteria

To check which criteria the top 5 countries perform best in we have used radar charts from Figure 4 onwards. A radar chart is a useful way to depict multi-variate observations. Each criteria is rated out of a total 10 points and all the 10 criteria are plotted together on the radar chart along with moisture percentage to understand how a particular country performs on individual criteria. The top-5 coffee bean producing countries according to our analysis are Ethiopia, Brazil, United States, Indonesia and Peru.

Table 3.2: Means for different Individual grading criterias
country_of_origin ma mfl maf mac mb mba mu mc ms mcu mm
Brazil 7.606667 7.548182 7.363939 7.464545 7.532727 7.543030 9.757273 9.69697 9.939091 7.497576 0.0803030
Ethiopia 8.001429 8.154286 7.892857 8.154286 7.930000 8.012857 9.904286 10.00000 10.000000 8.141429 0.0885714
Indonesia 7.682000 7.416000 7.200000 7.214000 7.600000 7.230000 9.866000 10.00000 9.866000 7.268000 0.0700000
Peru 7.446667 7.333333 7.223333 7.386667 7.530000 7.446667 9.776667 10.00000 10.000000 7.306667 0.1100000
United States 7.790000 7.875000 7.670000 7.875000 7.790000 7.670000 9.665000 9.66500 8.710000 7.835000 0.0000000

After looking at these plots, the conclusion drawn are as follows: The common characteristics that these top 5 countries have are the consistent higher values of uniformity and clean cup. Among all these countries, it can be seen that the country Ethiopia has the highest values for all the different characteristics that we have proven to have a significant affect on the quality of the coffee beans in the above sections. It is also interesting how the sweetness has a perfect score of 10 in all other countries other than United States as depicted.

3.6 Leading Regions

To respond to our final question Which regions/companies perform better than others in the quality of the coffee beans produced, intra-country? we use a barplot in Figure 6. The x-axis shows the total points a coffe bean recieves and the y-axis depicts the different regions where they were produced. The bars are coloured according to the country they belong to. Evidently Guji-Hambela is a region in Ethiopia where coffee beans get consistent good ratings and is the best region to grow coffee beans.

On the other hand, the figure also indicate that out of top 10 average coffee bean rating regions, Ethiopia takes over 4 regions, which also correspond to the statement made previously that Ethiopia is the top country that produces quality coffee beans.

Best Regions for the quality of coffee beans

Figure 3.4: Best Regions for the quality of coffee beans

3.7 Largest Producer

We also observe that Colombia is the largest producer of coffee beans Arabica variant with more than 40000 bags followed by Guatemala and Brazil. We establish using a bar-plot in Figure 7 with y-axis representing the number of bags produced and x-axis representing different countries. The Figure shows the top-6 coffee bean producing countries.

Largest producer country-wise

Figure 3.5: Largest producer country-wise

4 References